Let Navya take the complexity out of treatment decision making.

Our Story

Navya was inspired by our own struggles to find the best cancer treatment option, and our realization of how technology could simplify the process. This is our story.

Personal Experience as Patients

In 2007, Gitika Srivastava learned that one of her closest family members had received a cancer diagnosis. Her family was shell shocked. They scrambled to gather opinions from several oncologists, but the search was far from easy. They sent emails. They made phone calls. They asked extended family members and coworkers for referrals and recommendations. To find specialists, they tried everything they knew.

As it turned out, finding oncologists was only half the battle. Throughout the process, in order to communicate effectively with experts, they had to take on the monumental task of learning a medical vocabulary wholly unfamiliar to them. This meant that, in addition to speaking with oncologists, they spent a great deal of time conducting independent research using internet resources. They sat through nights of discussions and internet research to make a decision between Treatments 1 and 2, and then between Treatments 1(a), 1(b), and 1(c). There were multiple complex decision points throughout the period of care, each of which was equally important to the outcome.

Gitika’s relative’s case illustrates how important it is for patients to be involved in their care. For example, in the midst of a chaotic conference call with three experts, each at a different cancer center, he learned that one of the treatment protocols he was considering offered demonstrably better outcomes for patients in his age group. However, to take advantage of it, he would have to leave home and travel to a place foreign to him. Despite the stress of travel, he decided that following the data and expertise was most important. He decided to leave home to pursue the new treatment. Patient preference matters.

This was not an easy process. It was nerve wrecking, frightening, and confusing. To approach this ordeal, he and his family were determined to find the best information, the best treatment options, and the best recommendations from the best doctors. Once a treatment decision had been made, they could do nothing but leave the outcome up to fate and their faith in God. This is where Navya was born. By providing patients with the best information available, Navya seeks to deliver treatment recommendations that offer clarity in a stressful, confusing time.

Professional Experience as Physicians

As a resident, Dr. Naresh Ramarajan was confronted with countless treatment decisions in the Emergency Room and Intensive Care Unit. Diagnosis is a challenge, but for complex diseases like cancer, post-diagnosis treatment is equally challenging. He saw how physicians relied on a multitude of resources to decide which treatment option is best for a patient. To make one recommendation, a physician needs to manually synthesize information from clinical journals, medical encyclopedias, general guidelines, and cancer tumor boards. No two physicians always agree, and an expert’s experience with previous patients is often just as important as the medical literature.The final decision is not always obvious. It is a challenge for physicians to consolidate every piece of primary and secondary data, especially at the point of care.

Recent updates to the technology systems used for clinical decisions had also started to shape Naresh’s experience as a physician. He began to wonder how doctors could use technology to apply data from worldwide clinical trials to a single patient’s clinical diagnosis. How can one analyze primary data at the patient level to decide if a certain treatment studied in a clinical trial is relevant to the care of a patient? To go one step further, he wondered if it was possible to use machine learning to develop a system that grows smarter each time it helps a patient.

Navya was created to address these goals and challenges, and to create a decision system that a physician could use at the point of care.